We propose an interaction tree (IT) procedure to optimize the subgroup analysis in comparative studies that involve censored survival times. The proposed method recursively partitions the data into two subsets that show the greatest interaction with the treatment, which results in a number of objectively defined subgroups: in some of them the treatment effect is prominent while in others the treatment may have a negligible or even negative effect. The resultant tree structure can be used to explore the overall interaction between treatment and other covariates and help identify and describe possible target populations on which an experimental treatment demonstrates desired efficacy. We follow the standard CART (Breiman, et al., 1984) methodology to develop the interaction tree structure. Variable importance information is extracted via random forests of interaction trees. Both simulated experiments and an analysis of the primary billiary cirrhosis (PBC) data are provided for evaluation and illustration of the proposed procedure.

Summary We propose a new sparse estimation method for Cox (1972) proportional hazards models by optimizing an approximated information criterion. The main idea involves approximation of the ℓ0 norm with a continuous or smooth unit dent function. The proposed method bridges the best subset selection and regularisation by borrowing strength from both. It mimics the best subset selection using a penalised likelihood approach yet with no need of a tuning parameter. We further reformulate the problem with a reparameterisation step so that it reduces to one unconstrained nonconvex yet smooth programming problem, which can be solved efficiently as in computing the maximum partial likelihood estimator (MPLE). Furthermore, the reparameterisation tactic yields an additional advantage in terms of circumventing post-selection inference. The oracle property of the proposed method is established. Both simulated experiments and empirical examples are provided for assessment and illustration.

Dietary intervention trials aim to change dietary patterns of individuals. Participating in such trials could impact dietary self-report in divergent ways: Dietary counseling and training on portion-size estimation could improve self-report accuracy; participant burden could increase systematic error. Such intervention-associated biases could complicate interpretation of trial results. The authors investigated intervention-associated biases in reported total carotenoid intake using data on 3,088 breast cancer survivors recruited between 1995 and 2000 and followed through 2006 in the Women's Healthy Eating and Living Study, a randomized intervention trial. Longitudinal data from 2 self-report methods (24-hour recalls and food frequency questionnaires) and a plasma carotenoid biomarker were collected. A flexible measurement error model was postulated. Parameters were estimated in a Bayesian framework by using Markov chain Monte Carlo methods. Results indicated that the validity (i.e., correlation with "true" intake) of both self-report methods was significantly higher during follow-up for intervention versus nonintervention participants (4-year validity estimates: intervention = 0.57 for food frequency questionnaires and 0.58 for 24-hour recalls; nonintervention = 0.42 for food frequency questionnaires and 0.48 for 24-hour recalls). However, within- and between-instrument error correlations during follow-up were higher among intervention participants, indicating an increase in systematic error. Diet interventions can impact measurement errors of dietary self-report. Appropriate statistical methods should be applied to examine intervention-associated biases when interpreting results of diet trials.

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Objective To determine if the selective vasopressin type 1a receptor (V1aR) agonist selepressin (FE 202158) is as effective as the mixed V1a/V2 receptor (V1aR/V2R) agonist vasopressor hormone arginine vasopressin (AVP) when used as a titrated first-line vasopressor therapy in an ovine model of Pseudomonas aeruginosa pneumonia-induced severe sepsis. Design Prospective, randomized, controlled laboratory experiment. Setting University animal research facility. Subjects Forty-five chronically instrumented sheep. Interventions Sheep were anesthetized, insufflated with cooled cotton smoke via tracheostomy, and P. aeruginosa were instilled into their airways. They were then placed on assisted ventilation, awakened, and resuscitated with lactated Ringer's solution titrated to maintain hematocrit ± 3% from baseline levels. If, despite fluid management, mean arterial pressure (MAP) fell by > 10 mm Hg from baseline levels, a continuous i.v. infusion of AVP or selepressin was titrated to raise and maintain MAP within 10 mm Hg of baseline. Effects of combination treatment of selepressin with the selective V2R agonist desmopressin were similarly investigated. Measurements and Main Results In septic sheep, MAP fell by ~30 mm Hg, systemic vascular resistance index (SVRI) decreased by ~50%, and ~7 L of fluid were retained over 24 h; this fluid accumulation was partially reduced by AVP and almost completely blocked by selepressin; combined infusion of selepressin and desmopressin increased fluid accumulation to levels similar to AVP treatment. Conclusions Resuscitation with the selective V1aR agonist selepressin blocked vascular leak more effectively than the mixed V1aR/V2R agonist AVP because of its lack of agonist activity at the V2R.

SUMMARYThis paper is concerned with developing rules for assignment of tooth prognosis based on actual tooth loss in the VA Dental Longitudinal Study. It is also of interest to rank the relative importance of various clinical factors for tooth loss. A multivariate survival tree procedure is proposed. The procedure is built on a parametric exponential frailty model, which leads to greater computational efficiency. We adopted the goodness-of-split pruning algorithm of LeBlanc and Crowley (1993) to determine the best tree size. In addition, the variable importance method is extended to trees grown by goodness-of-fit using an algorithm similar to the random forest procedure in Breiman (2001). Simulation studies for assessing the proposed tree and variable importance methods are presented. To limit the final number of meaningful prognostic groups, an amalgamation algorithm is employed to merge terminal nodes that are homogenous in tooth survival. The resulting prognosis rules and variable importance rankings seem to offer simple yet clear and insightful interpretations.

Background: Small nucleolar RNA host gene 7 (SNHG7) is a novel identified oncogenic gene in tumorigenesis. However, the role that SNHG7 plays in pancreatic cancer (PC) remains unclear. In this study, we aimed to investigate the functional effects of SNHG7 on PC and the possible mechanism. Methods: The expression levels of SNHG7 in tissues and cell lines were measured by RT-qPCR. Cell viability, apoptosis, migration and invasion were examined to explore the function of SNHG7 on PC. Bioinformatics methods were used to predict the target genes. The mechanism was further investigated by transfection with specific si-RNA, miRNA mimics or miRNA inhibitor. Tumor xenograft was carried out to verify the effects of SNHG7 in vivo. Results: We found that SNHG7 was overexpressed in both PC tissues and cell lines. High expression level of SNHG7 was correlated with the poor prognosis. SNHG7 knockdown inhibited the proliferation, migration and invasion of PC cells. Moreover, SNHG7 was found to regulate the expression of ID4 via sponging miR-342-3p. Additionally, this finding was supported by in vivo experiments. Conclusions: LncRNA SNHG7 was overexpressed in PC tissues, and knockdown of SNHG7 suppressed PC cell proliferation, migration and invasion via miR-342-3p/ID4 axis. The results indicated that SNHG7 as a potential target for clinical treatment of PC.

The development of an accurate prognosis is an integral component of treatment planning in the practice of periodontics. Prior work has evaluated the validity of using various clinical measured parameters for assigning periodontal prognosis as well as for predicting tooth survival and change in clinical conditions over time. We critically review the application of multivariate Classification And Regression Trees (CART) for survival in developing evidence-based periodontal prognostic indicators. We focus attention on two distinct methods of multivariate CART for survival: the marginal goodness-of-fit approach, and the multivariate exponential approach. A number of common clinical measures have been found to be significantly associated with tooth loss from periodontal disease, including furcation involvement, probing depth, mobility, crown-to-root ratio, and oral hygiene. However, the inter-relationships among these measures, as well as the relevance of other clinical measures to tooth loss from periodontal disease (such as bruxism, family history of periodontal disease, and overall bone loss), remain less clear. While inferences drawn from any single current study are necessarily limited, the application of new approaches in epidemiologic analyses to periodontal prognosis, such as CART for survival, should yield important insights into our understanding, and treatment, of periodontal diseases.